Optimal Control for Industrial Sucrose Crystallization with Action Dependent Heuristic Dynamic Programming
Автор: Xiaofeng Lin, Heng Zhang, Li Wei, Huixia Liu
Журнал: International Journal of Image, Graphics and Signal Processing(IJIGSP) @ijigsp
Статья в выпуске: 1 vol.1, 2009 года.
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This paper applies a neural-network-based approximate dynamic programming (ADP) method, namely, the action dependent heuristic dynamic programming (ADHDP), to an industrial sucrose crystallization optimal control problem. The industrial sucrose crystallization is a nonlinear and slow time-varying process. It is quite difficult to establish a precise mechanism model of the crystallization, because of complex internal mechanism and interacting variables. We developed a neural network model of the crystallization based on the data from the actual sugar boiling process of sugar refinery. The ADHDP is a learningand approximation-based approach which can solve the optimization control problem of nonlinear system. The paper covers the basic principle of this learning scheme and the design of neural network controller based on the approach. The result of simulation shows the controller based on action dependent heuristic dynamic programming approach can optimize industrial sucrose crystallization.
Sucrose Crystallization, Sugar Boiling, Neural Networks, Approximate Dynamic Programming, Action Dependent Heuristic Dynamic Programming
Короткий адрес: https://sciup.org/15011953
IDR: 15011953
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